As the performance and capabilities of generative artificial intelligence (GenAI) applications progress rapidly, Shaun Lee, managing director, private markets segment at State Street in Luxembourg, foresees the technology reshaping private markets significantly, influencing everything from decision-making processes to operational efficiencies.

GenAI in private markets

Lee identifies several promising uses of GenAI in private markets, emphasising its potential to transform investment management and servicing. He highlights investment research and analysis, where GenAI’s ability to process large datasets and generate actionable insights could revolutionise the field. This includes analysing financial statements, market trends and economic indicators for a deeper understanding of investments.

In deal sourcing and due diligence, GenAI can automate the identification of opportunities and enhance due diligence by quickly processing large volumes of documents like legal contracts and financial reports to uncover risks and opportunities.

For portfolio management, GenAI enables the creation of sophisticated valuation models that adapt to real-time market changes, supporting fund managers in making better decisions. AI can also improve scenario analysis and forecasting, allowing private market funds to better anticipate and prepare for different economic conditions, including extreme market scenarios.

AI’s ability to customise financial products to investor needs is another key benefit. By analysing investor profiles, AI can develop personalised investment solutions that align with client goals.

Lee stresses that these GenAI advancements can lead to greater operational efficiency, better decision-making and improved outcomes for investors and private market funds. He notes that GenAI has the potential to significantly impact private markets and alternative investments from both a management and servicing perspective.

data privacy and security is key; typically, handling vast amounts of sensitive financial data increases the risk of cyberattacks and data breaches

Shaun Lee, Managing director, private markets, State Street

Decision-making through AI

The use of GenAI can significantly improve decision-making for private market funds, says Lee, outlining a few ways in which AI achieves this.

AI’s ability to process and analyse both structured and unstructured data from diverse sources can, for instance, uncover trends and correlations often missed by traditional methods. This in-depth analysis offers fund managers deeper insights into market dynamics.

With predictive modelling, AI can forecast future market conditions and performance trends, enabling managers to make proactive decisions based on anticipated changes.

AI also accelerates due diligence by scanning and summarising large volumes of data and documents efficiently, speeding up evaluations while ensuring critical details are captured.

Furthermore, AI can conduct complex market simulations and stress tests, helping funds understand how their strategies might perform under various economic scenarios, including extreme market conditions.

Ethical concerns and risks

While GenAI presents numerous opportunities, Lee also addresses several ethical concerns and risks associated with its use in private markets, emphasising that the integration of AI must be handled with care. “First, data privacy and security is key; typically, handling vast amounts of sensitive financial data increases the risk of cyberattacks and data breaches,” he explains, stressing that ensuring data privacy and security is paramount. The financial sector, in particular, is highly vulnerable, and as AI is increasingly employed in decision-making, robust cybersecurity measures must be implemented. Lee advocates for stringent protocols that limit access to data and models, ensuring that only authorised personnel are involved, thereby mitigating the risks associated with the use of GenAI in private markets.

A further concern is model bias and fairness. “AI models trained on biased historical data may perpetuate or even exacerbate existing biases in investment decisions, [and this] must also be considered when developing models,” Lee warns. This issue, he points out, is particularly pressing in private markets, where data can be sparse and not as standardised as in public markets. Without careful consideration of the data used to train AI models, there is a risk that the technology could reinforce pre-existing disparities rather than address them. For Lee, developing fair and unbiased AI models is not only a technical challenge but also an ethical imperative that must be prioritised as AI continues to shape investment strategies.

AI models are not infallible and can make mistakes

Shaun Lee, Managing director, private markets, State Street

Avoid over-reliance

Lee expresses caution about over-reliance on AI. While artificial intelligence can provide valuable insights, “AI models are not infallible and can make mistakes,” he says, and human oversight remains essential. In fact, he highlights the danger of reducing human involvement in critical decision-making processes, as relying too heavily on AI could lead to market manipulation or destabilisation. Lee draws attention to the unpredictable nature of AI, particularly when multiple systems interact in ways that could have unforeseen consequences. AI models are tools, not replacements for human judgment, Lee stresses, underscoring the importance of maintaining a balanced approach that integrates AI with human expertise to avoid significant financial or operational disruptions.

Another key area that Lee focuses on is regulatory compliance, which is becoming more complex as AI technologies evolve. “It is important to engage with regulators proactively.” He points out the importance of shaping appropriate guidelines and standards for AI and GenAI usage in private markets. As regulations around data privacy and financial markets continue to develop, Lee advises that private market funds work closely with regulatory bodies to ensure that AI technologies are not only compliant but also ethically sound. He suggests that companies in the private markets space should view regulatory engagement as an opportunity to lead the way in establishing best practices for AI integration.

Lee reiterates the need for a strategic approach, and a human element must always remain in the decision-making process to avoid potential pitfalls like “unintended market manipulation or destabilisation, especially if multiple AI systems interact in unpredictable ways.” He believes that the future of AI and GenAI in private markets will depend heavily on how carefully and responsibly firms manage these risks, while also staying ahead of the curve through innovation. Lee urges firms to take a balanced approach that recognises both the transformative potential and the limitations of AI.

AI in due diligence and risk assessment

One of the most practical uses of GenAI that Lee highlights is in due diligence. “AI can generate concise summaries of lengthy documents, highlighting the most important points and potential red flags,” Lee explains. This automation not only saves time but also adds a layer of accuracy to the due diligence process.

This efficiency is further enhanced by natural language processing (NLP), which can parse large volumes of text from contracts, legal documents and financial reports. According to Lee, integrating GenAI into due diligence will ultimately allow private market funds to “achieve greater efficiency, accuracy and depth in their evaluations, ultimately leading to more informed and confident investment decisions.”

Real-world applications

A notable example of GenAI in practice is State Street’s Alpha for Private Markets platform. “We use natural language processing to parse large volumes of unstructured data contained in spreadsheets, call transcripts and prospectuses.” This advanced platform enables alternative asset managers to evaluate investment opportunities more quickly, giving them a competitive edge. The insights it yields “has the potential to streamline manual workflows that previously required significant numbers of analysts,” claims Lee.

Limitations and looking ahead

Lee acknowledges the limitations of GenAI, noting that inconsistent data often defines private markets. He highlights that private markets lack the depth and breadth of data available in public markets, which can reduce the effectiveness of AI models, particularly in novel situations.

Lee also stresses the need for a balance between AI and human oversight. He warns that over-reliance on AI can diminish critical human judgement, especially in financial and legal evaluations. While AI offers valuable insights, it should not replace human expertise.

Looking ahead, Lee advises private market funds to adopt a strategic approach to AI. He suggests starting with small pilot projects to test GenAI capabilities and focusing on high-impact applications like due diligence and risk assessment. Ensuring high-quality data and partnering with experienced providers are also essential for successful AI integration.

Lee concludes that a thoughtful approach to AI, combining innovation with human expertise, will help private market funds maintain a competitive edge while navigating the challenges and opportunities of GenAI.

The Alfi private assets conference on 26 September will feature a panel discussion on generative AI.

An alternate version of this article first appeared in the October 2024 supplement of Paperjam magazine.